Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations

• URL: http://arxiv.org/abs/2305.12715v3
• Date: Fri, 29 Sep 2023 06:23:52 GMT
• Title: Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations
• Authors: Hao Chen, Ankit Shah, Jindong Wang, Ran Tao, Yidong Wang, Xing Xie, Masashi Sugiyama, Rita Singh, Bhiksha Raj
• Abstract summary: imprecise label learning (ILL) is a framework for the unification of learning with various imprecise label configurations. We demonstrate that ILL can seamlessly adapt to partial label learning, semi-supervised learning, noisy label learning, and, more importantly, a mixture of these settings.
• Score: 95.12263518034939
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning with reduced labeling standards, such as noisy label, partial label, and multiple label candidates, which we generically refer to as \textit{imprecise} labels, is a commonplace challenge in machine learning tasks. Previous methods tend to propose specific designs for every emerging imprecise label configuration, which is usually unsustainable when multiple configurations of imprecision coexist. In this paper, we introduce imprecise label learning (ILL), a framework for the unification of learning with various imprecise label configurations. ILL leverages expectation-maximization (EM) for modeling the imprecise label information, treating the precise labels as latent variables.Instead of approximating the correct labels for training, it considers the entire distribution of all possible labeling entailed by the imprecise information. We demonstrate that ILL can seamlessly adapt to partial label learning, semi-supervised learning, noisy label learning, and, more importantly, a mixture of these settings. Notably, ILL surpasses the existing specified techniques for handling imprecise labels, marking the first unified framework with robust and effective performance across various challenging settings. We hope our work will inspire further research on this topic, unleashing the full potential of ILL in wider scenarios where precise labels are expensive and complicated to obtain.

Related papers

• Determined Multi-Label Learning via Similarity-Based Prompt [12.428779617221366]
  In multi-label classification, each training instance is associated with multiple class labels simultaneously. To alleviate this problem, a novel labeling setting termed textitDetermined Multi-Label Learning (DMLL) is proposed.
  arXiv  Detail & Related papers  (2024-03-25T07:08:01Z)
• Towards Imbalanced Large Scale Multi-label Classification with Partially Annotated Labels [8.977819892091]
  Multi-label classification is a widely encountered problem in daily life, where an instance can be associated with multiple classes. In this work, we address the issue of label imbalance and investigate how to train neural networks using partial labels.
  arXiv  Detail & Related papers  (2023-07-31T21:50:48Z)
• Exploring Structured Semantic Prior for Multi Label Recognition with Incomplete Labels [60.675714333081466]
  Multi-label recognition (MLR) with incomplete labels is very challenging. Recent works strive to explore the image-to-label correspondence in the vision-language model, ie, CLIP, to compensate for insufficient annotations. We advocate remedying the deficiency of label supervision for the MLR with incomplete labels by deriving a structured semantic prior.
  arXiv  Detail & Related papers  (2023-03-23T12:39:20Z)
• PLMCL: Partial-Label Momentum Curriculum Learning for Multi-Label Image Classification [25.451065364433028]
  Multi-label image classification aims to predict all possible labels in an image. Existing works on partial-label learning focus on the case where each training image is annotated with only a subset of its labels. This paper proposes a new partial-label setting in which only a subset of the training images are labeled, each with only one positive label, while the rest of the training images remain unlabeled.
  arXiv  Detail & Related papers  (2022-08-22T01:23:08Z)
• One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement [71.9401831465908]
  We investigate single-positive multi-label learning (SPMLL) where each example is annotated with only one relevant label. A novel method named proposed, i.e., Single-positive MultI-label learning with Label Enhancement, is proposed. Experiments on benchmark datasets validate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2022-06-01T14:26:30Z)
• Acknowledging the Unknown for Multi-label Learning with Single Positive Labels [65.5889334964149]
  Traditionally, all unannotated labels are assumed as negative labels in single positive multi-label learning (SPML) We propose entropy-maximization (EM) loss to maximize the entropy of predicted probabilities for all unannotated labels. Considering the positive-negative label imbalance of unannotated labels, we propose asymmetric pseudo-labeling (APL) with asymmetric-tolerance strategies and a self-paced procedure to provide more precise supervision.
  arXiv  Detail & Related papers  (2022-03-30T11:43:59Z)
• Cost-Accuracy Aware Adaptive Labeling for Active Learning [9.761953860259942]
  In many real settings, different labelers have different labeling costs and can yield different labeling accuracies. We propose a new algorithm for selecting instances, labelers and their corresponding costs and labeling accuracies. Our proposed algorithm demonstrates state-of-the-art performance on five UCI and a real crowdsourcing dataset.
  arXiv  Detail & Related papers  (2021-05-24T17:21:00Z)
• A Study on the Autoregressive and non-Autoregressive Multi-label Learning [77.11075863067131]
  We propose a self-attention based variational encoder-model to extract the label-label and label-feature dependencies jointly. Our model can therefore be used to predict all labels in parallel while still including both label-label and label-feature dependencies.
  arXiv  Detail & Related papers  (2020-12-03T05:41:44Z)
• Learning Soft Labels via Meta Learning [3.4852307714135375]
  One-hot labels do not represent soft decision boundaries among concepts, and hence, models trained on them are prone to overfitting. We propose a framework, where we treat the labels as learnable parameters, and optimize them along with model parameters. We show that learned labels capture semantic relationship between classes, and thereby improve teacher models for the downstream task of distillation.
  arXiv  Detail & Related papers  (2020-09-20T18:42:13Z)
• Learning to Purify Noisy Labels via Meta Soft Label Corrector [49.92310583232323]
  Recent deep neural networks (DNNs) can easily overfit to biased training data with noisy labels. Label correction strategy is commonly used to alleviate this issue. We propose a meta-learning model which could estimate soft labels through meta-gradient descent step.
  arXiv  Detail & Related papers  (2020-08-03T03:25:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.